MindsDB's federated query engine represents a revolutionary data query paradigm with three core differences over traditional approaches:
- Architecture level: the traditional approach requires data to be centralized in a data warehouse (e.g., Hadoop) in order to be analyzed; the federation engine achieves this through a virtual data layerLogically centralized but physically decentralizedconsultancy
- performance optimization: Adopt intelligent query push down technology, distribute the calculation task to each data source for execution, and finally aggregate the results, which improves the efficiency of querying again after ETL by 3-5 times.
- Functionality Expansion::
- Native cross-origin JOIN operations (e.g., MySQL tables associated with Slack messages)
- Built-in automatic data type conversion
- Real-time optimization of query plans
Examples of typical application scenarios:
The marketing team needed to analyze data correlating email marketing (Gmail) with customer work orders (Zendesk), which the federation engine could perform directlySELECT gmail.subject, zendesk.ticket_status FROM gmail_emails JOIN zendesk_tickets ON gmail.customer_id=zendesk.customer_idWith such cross-origin queries, the entire process requires no data migration.
This answer comes from the articleMindsDB: An Open Source Platform for Connecting Data from Multiple Sources and Querying with SQL and AIThe
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